基于超局部化时间序列的永磁同步电机无模型预测电流滑模控制策略

doi:10.19595/j.cnki.1000-6753.tces.230301

Abstract
Figure/Table
References (0)
Related Citation (1)

Download: PDF (1410 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Considering the limited flexibility and robustness of the typical sliding mode control (SMC), it falls short in meeting the demands of a complex environment with variable load and the influence of time-varying inductance parameters, magnetic field coupling, core saturation, and other factors. On the contrary, the model-free SMC strategy proves to be more effective in overcoming these challenges, with the main concern being the accuracy of the model that affects control performance. To tackle this issue, this paper proposes a model-free predictive SMC strategy utilizing an ultra-localized time-series model for a permanent magnet synchronous motor (PMSM) driving system. By representing the motor as a collection of discrete-time linear functions and maintaining high model accuracy through an online estimation algorithm, the proposed strategy is better suited to the motion characteristics of the motor system.
Firstly, this approach establishes an ultra-localized time-series model and updates the regressive vector, which summarizes input and output signals based on sampled data only. Secondly, all undetermined coefficients in the model are estimated using the recursive least square (RLS) algorithm. Consequently, the current operating state of the motor driving system is described as a collection of discrete-time linear functions and converted into the ultra-local structure to generate the sliding mode signal. Finally, the control functions are designed based on the power reaching rule, and the reaching conditions are verified using the Lyapunov method. This ultra-localized time-series model is easily implemented within the SMC strategy, offering good accuracy and addressing the issues caused by time-varying physical parameters in the plant’s model of the typical SMC and input gain of the conventional observer-based ultra-local model.
Simulation and experimental results on a PMSM driving system demonstrate the effectiveness of the proposed method in resisting disturbances and successfully tracking the reference. The disturbances primarily include changed parameter mismatches and load torque. Fourier analysis and accumulated error comparisons between the proposed and conventional model-free SMC strategies show that the proposed method improves current quality and model accuracy by reducing the total harmonic distortion (THD) of 3.14%. Compared to the conventional strategy, the proposed method exhibits the minimum ascending slope of accumulated error for current and lower operating noise amplitudes in various speed references and load torques. These results highlight the effectiveness and convergence of the ultra-localized time-series model-based SMC strategy with the estimation algorithm. To further validate its robustness, experimental results are obtained with different parameter mismatches of the stator inductance, and are compared using continuous Fourier analysis calculations.
The validations provide the following conclusions: 1) The proposed method adopts an ultra-localized time-series model to represent the current operating state of the motor driving system. Unlike the conventional strategy that utilizes an ultra-local approach, this model formulates the system as a collection of discrete-time linear functions. 2) The ultra-localized time-series model significantly improves current quality, accumulated current error, and system noise compared to the conventional strategy. This improvement is attributed to the high accuracy of the ultra-localized time-series model and the eliminated influences of the unsuitable input gain. 3) By employing a designed estimation algorithm and sampled data, the ultra-localized time-series model replaces the physical model, which involves multiple time-varying physical parameters of the system. This replacement enables more accurate modeling and updating processes.

Key words： Model-free sliding mode control ultra-localized time series model recursive least square algorithm data-driven model

PACS:

TM341

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yao Wei
	Dongliang Ke
	Dongxiao Huang
	Fengxiang Wang
	Zhenbin Zhang

Cite this article:

Yao Wei,Dongliang Ke,Dongxiao Huang等. Model-Free Predictive Sliding Mode Control Using Ultra-Localized Time-Series for PMSM Drives[J]. Transactions of China Electrotechnical Society, 0, (): 239610-239610.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230301 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/239610

[1] 李婕, 杨淑英, 谢震, 等. 基于有效信息迭代快速粒子群优化算法的永磁同步电机参数在线辨识[J]. 电工技术学报, 2022, 37(18): 4604-4613.
Li Jie, Yang Shuying, Xie Zhen, et al.Online Parameter Identification of Permanent Magnet Synchronous Motor Based on Fast Particle Swarm Optimization Algorithm with Effective Information Iterated[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4604-4613.
[2] 李晓华, 赵容健, 田晓彤, 等. 逆变器供电对电动汽车内置式永磁同步电机振动噪声特性影响研究[J]. 电工技术学报, 2020, 35(21): 4455-4464.
Li Xiaohua, Zhao Rongjian, Tian Xiaotong, et al.Study on Vibration and Noise Characteristics of Interior Permanent Magnet Synchronous Machine for Electric Vehicles by Inverter[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4455-4464.
[3] Rodriguez J., Garcia C., Mora A., et al.Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies[J]. IEEE Transactions on Power Electronics, 2022, 37(4): 3927-3942.
[4] Tang M., Benedetto Marco D., Bifaretto S., et al.State of the Art of Repetitive Control in Power Electronics and Drive Applications[J]. IEEE Open Journal of Industry Applications, 2022, 3: 13-29.
[5] Yu X., Feng Y., Man Z.Terminal Sliding Mode Control-An Overview[J]. IEEE Open Journal of the Industrial Electronics Society, 2021, 2: 36-52.
[6] 黄依婷, 沈建新, 王云冲, 等. 基于递推最小二乘法观测器的永磁同步伺服电机变参数滑模控制[J]. 中国电机工程学报, 2022, 42(18): 6835-6846.
Huang Yiting, Shen Jianxin, Wang Yunchong, et al.Variable Parameter Sliding Mode Control of Permanent Magnet Synchronous Servo Machine Based on Recursive Least Square Observer[J]. Proceedings of the CSEE, 2022, 42(18): 6835-6846.
[7] 付东学, 赵希梅. 永磁直线同步电机自适应反推全局快速终端滑模控制[J]. 电工技术学报, 2020, 35(8): 1634-1641.
Fu Dongxue, Zhao Ximei.Adaptive Backstepping Global Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1634-1641.
[8] 方馨, 王丽梅, 张康. 基于扰动观测器的永磁直线电机高阶非奇异快速终端滑模控制[J]. 电工技术学报, 2023, 38(2): 409-421.
Fang Xin, Wang Limei, Zhang Kang.High Order Nonsingular Fast Terminal Sliding Mode Control of Permanent Magnet Linear Motor Based on Disturbance Observer[J]. Transactions of China Electrotechnical Society, 2023, 38(2): 409-421.
[9] El-Sousy F.F. M., Alenizi F. A. F. Optimal Adaptive Super-Twisting Sliding-Mode Control Using Online Actor-Critic Neural Networks for Permanent-Magnet Synchronous Motor Drives[J]. IEEE Access, 2021, 9: 82508-82534.
[10] 韩国强, 陆哲, 吴孟霖, 等. 基于改进滑模控制策略的开关磁阻电机直接瞬时转矩控制方法[J]. 电工技术学报, 2022, 37(22): 5740-5755.
Han Guoqiang, Lu Zhe, Wu Menglin, et al.Direct Instantaneous Torque Control Method for Switched Reluctance Motor Based on an Improved Sliding Mode Control Strategy[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5740-5755.
[11] Yuan L., Jiang Y., Xiong L., et al.Sliding mode control approach with integrated disturbance observer for PMSM speed system[J/OL]. CES Transactions on Electrical Machines and Systems, 2023, doi: 10.30941/CESTEMS.2023.00009.
[12] 魏惠芳, 王丽梅. 永磁直线同步电机自适应模糊神经网络时变滑模控制[J]. 电工技术学报, 2022, 37(4): 861-869.
Wei Huifang, Wang Limei.Adaptive Fuzzy Neural Network Time-Varying Sliding Mode Control for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2022, 37(4): 861-869.
[13] Wang F., He L.FPGA-Based Predictive Speed Control for PMSM System Using Integral Sliding-Mode Disturbance Observer[J]. IEEE Transactions on Industrial Electronics, 2021, 68(2): 972-981.
[14] 王琛琛, 苟立峰, 周明磊, 等. 基于改进的离散域二阶滑模观测器的内置式永磁同步电机无位置传感器控制[J]. 电工技术学报, 2023, 38(2): 387-397.
Wang Chenchen, Gou Lifeng, Zhou Minglei, et al.Sensorless Control of IPMSM Based on Improved Discrete Second-Order Sliding Mode Observer[J]. Transactions of China Electrotechnical Society, 2023, 38(2): 387-397.
[15] Li W., Yuan H., Li S., et al.A Revisit to Model-Free Control[J]. IEEE Transactions on Power Electronics, 2022, 37(12): 14408-14421.
[16] 米阳, 伦雪莹, 孟凡斌, 等. 基于无模型算法和电动汽车辅助调节的新能源电力系统频率协调控制[J]. 电力系统保护与控制, 2021, 49(24): 13-20.
Mi Yang, Lun Xueying, Meng Fanbin, et al.Frequency Coordinated Control of a Now Energy Power System basd on a Model-Free Algorithm and EV Auxiliary Regulation[J]. Power System Protection and Control, 2021, 49(24): 13-20.
[17] 曹荣敏, 郑鑫鑫, 侯忠生. 基于改进多入多出无模型自适应控制的二维直线电机迭代学习控制[J]. 电工技术学报, 2021, 36(19): 4025-4034.
Cao Rongmin, Zheng Xinxin, Hou Zhongsheng.An Iterative Learning Control Based on Improved Multiple Input and Multiple Output Model Free Adaptive Control for Two-Dimensional Linear Motor[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4025-4034.
[18] Wang F., Wei Y., Young H., et al.Continuous-Control-Set Model-Free Predictive Fundamental Current Control for PMSM System[J/OL]. Transactions on Power Electronics, 2023, doi: 10.1109/TPEL.2023.3240282.
[19] Zhao K., Yin T., Zhang C., et al.Robust Model-Free Nonsingular Terminal Sliding Mode Control for PMSM Demagnetization Fault[J]. IEEE Access, 2019, 7: 15737-15748.
[20] Mousavi M., Alireza Davari S., Nekoukar V., et al.Predictive Torque Control of Induction Motor Based on a Robust Integral Sliding Mode Observer[J]. IEEE Transactions on Industrial Electronics, 2023, 70(3): 2339-2350.
[21] 汪凤翔, 杨奥, 于新红, 等. 基于自适应超螺旋滑模观测器的三相Vienna整流器无模型预测电流控制[J/OL]. 电工技术学报, 2023, doi: 10.19595/j.cnki.1000-6753.tces.222213.
Wang Fengxiang, Yang Ao, Yu Xinhong, et al.Model-Free Predictive Current Control for Three-Phase Vienna Rectifier Based on Adaptive Super-Twisting Sliding Mode Observer[J/OL]. Transactions of China Electrotechnical Society, 2023, doi: 10.19595/j.cnki.1000-6753.tces.222213.
[22] Wei Y., Young H., Wang F., et al.Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems[J/OL]. IEEE Transactions on Industrial Electronics, 2022, doi: 10.1109/TIE.2022.3210563.
[23] 刘国海, 陈仁杰, 张多, 等. 两电机调速系统的神经网络逆无模型自适应鲁棒解耦控制[J]. 中国电机工程学报, 2019, 39(3): 868-874.
Liu Guohai, Chen Renjie, Zhangduo, et al. Model-free Adaptive Robust Control for Two Motor Drive System Based on Neural Network Inversion[J]. Proceedings of the CSEE, 2019, 39(3): 868-874.
[24] Bolognani S., Carlet P.G., Tinazzi F., et al. Current Ripple Minimisation in Deadbeat Parameter-Free Predictive Control of Synchronous Motor Drives[J]. IEEE Open Journal of Industry Applications, 2021, 2: 278-288.
[25] Brosch A., Hanke S., Wallscheid O., et al.Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors[J]. IEEE Transactions on Power Electronics, 2021, 36(2): 2179-2190.
[26] 赵凯辉, 刘文昌, 刘智诚, 等. 一种永磁同步电机无模型高阶滑模控制算法[J/OL]. 电工技术学报, 2022, doi: 10.19595/j.cnki.1000-6753.tces.220615.
Zhao Kaihui, Liu Wenchang, Liu Zhicheng, et al.Model-free High Sliding Mode Control for Permanent Magnet Synchronous Motor[J/OL]. Transactions of China Electrotechnical Society, 2022, doi: 10.19595/j.cnki.1000-6753.tces.220615.
[27] 赵凯辉, 戴旺坷, 周瑞睿, 等. 基于扩展滑模扰动观测器的永磁同步电机新型无模型滑模控制[J]. 中国电机工程学报, 2022, 42(6): 2375-2386.
Zhao Kaihui, Dai Wangke, Zhou Ruirui, et al.Novel Model-free Sliding Mode Control of Permanent Magnet Synchronous Motor Based on Extended Sliding Mode Disturbance Observer[J]. Proceedings of the CSEE, 2022, 42(6): 2375-2386.
[28] 赵凯辉, 殷童欢, 张昌凡, 等. 永磁同步电机无模型滑模控制方法研究[J]. 电子测量与仪器学报, 2018, 32(4): 172-180.
Zhao Kaihui, Yin Tonghuan, Zhang Changfan, et al.Research on Model-free Sliding Mode Control of Permanent Magnet Synchronous Motor[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(4): 172-180.
[29] Zhang Y., Jin J., Huang L.Model-Free Predictive Current Control of PMSM Drives Based on Extended State Observer Using Ultralocal Model[J] IEEE Transactions on Industrial Electronics, 2021, 68(2): 993-1003.
[30] Li W., Yuan H., Li S., et al.A Revisit to Model-Free Control[J] IEEE Transactions on Power Electronics, 2022, 37(12): 14408-14421.